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535 lines
20 KiB
Python
535 lines
20 KiB
Python
from __future__ import annotations
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import copy
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import logging
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import re
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from abc import ABC, abstractmethod
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from collections.abc import Callable, Collection, Iterable, Sequence, Set
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from dataclasses import dataclass
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from typing import (
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Any,
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Literal,
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Optional,
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TypedDict,
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TypeVar,
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Union,
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)
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from core.rag.models.document import BaseDocumentTransformer, Document
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logger = logging.getLogger(__name__)
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TS = TypeVar("TS", bound="TextSplitter")
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def _split_text_with_regex(
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text: str, separator: str, keep_separator: bool
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) -> list[str]:
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# Now that we have the separator, split the text
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if separator:
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if keep_separator:
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# The parentheses in the pattern keep the delimiters in the result.
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_splits = re.split(f"({re.escape(separator)})", text)
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splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)]
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if len(_splits) % 2 == 0:
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splits += _splits[-1:]
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splits = [_splits[0]] + splits
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else:
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splits = re.split(separator, text)
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else:
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splits = list(text)
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return [s for s in splits if s != ""]
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class TextSplitter(BaseDocumentTransformer, ABC):
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"""Interface for splitting text into chunks."""
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def __init__(
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self,
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chunk_size: int = 4000,
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chunk_overlap: int = 200,
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length_function: Callable[[str], int] = len,
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keep_separator: bool = False,
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add_start_index: bool = False,
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) -> None:
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"""Create a new TextSplitter.
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Args:
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chunk_size: Maximum size of chunks to return
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chunk_overlap: Overlap in characters between chunks
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length_function: Function that measures the length of given chunks
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keep_separator: Whether to keep the separator in the chunks
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add_start_index: If `True`, includes chunk's start index in metadata
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"""
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if chunk_overlap > chunk_size:
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raise ValueError(
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f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
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f"({chunk_size}), should be smaller."
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)
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self._chunk_size = chunk_size
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self._chunk_overlap = chunk_overlap
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self._length_function = length_function
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self._keep_separator = keep_separator
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self._add_start_index = add_start_index
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@abstractmethod
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def split_text(self, text: str) -> list[str]:
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"""Split text into multiple components."""
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def create_documents(
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self, texts: list[str], metadatas: Optional[list[dict]] = None
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) -> list[Document]:
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"""Create documents from a list of texts."""
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_metadatas = metadatas or [{}] * len(texts)
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documents = []
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for i, text in enumerate(texts):
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index = -1
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for chunk in self.split_text(text):
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metadata = copy.deepcopy(_metadatas[i])
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if self._add_start_index:
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index = text.find(chunk, index + 1)
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metadata["start_index"] = index
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new_doc = Document(page_content=chunk, metadata=metadata)
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documents.append(new_doc)
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return documents
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def split_documents(self, documents: Iterable[Document]) -> list[Document]:
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"""Split documents."""
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texts, metadatas = [], []
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for doc in documents:
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texts.append(doc.page_content)
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metadatas.append(doc.metadata)
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return self.create_documents(texts, metadatas=metadatas)
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def _join_docs(self, docs: list[str], separator: str) -> Optional[str]:
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text = separator.join(docs)
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text = text.strip()
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if text == "":
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return None
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else:
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return text
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def _merge_splits(self, splits: Iterable[str], separator: str) -> list[str]:
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# We now want to combine these smaller pieces into medium size
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# chunks to send to the LLM.
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separator_len = self._length_function(separator)
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docs = []
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current_doc: list[str] = []
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total = 0
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for d in splits:
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_len = self._length_function(d)
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if (
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total + _len + (separator_len if len(current_doc) > 0 else 0)
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> self._chunk_size
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):
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if total > self._chunk_size:
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logger.warning(
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f"Created a chunk of size {total}, "
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f"which is longer than the specified {self._chunk_size}"
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)
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if len(current_doc) > 0:
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doc = self._join_docs(current_doc, separator)
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if doc is not None:
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docs.append(doc)
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# Keep on popping if:
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# - we have a larger chunk than in the chunk overlap
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# - or if we still have any chunks and the length is long
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while total > self._chunk_overlap or (
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total + _len + (separator_len if len(current_doc) > 0 else 0)
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> self._chunk_size
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and total > 0
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):
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total -= self._length_function(current_doc[0]) + (
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separator_len if len(current_doc) > 1 else 0
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)
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current_doc = current_doc[1:]
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current_doc.append(d)
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total += _len + (separator_len if len(current_doc) > 1 else 0)
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doc = self._join_docs(current_doc, separator)
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if doc is not None:
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docs.append(doc)
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return docs
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@classmethod
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def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
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"""Text splitter that uses HuggingFace tokenizer to count length."""
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try:
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from transformers import PreTrainedTokenizerBase
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if not isinstance(tokenizer, PreTrainedTokenizerBase):
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raise ValueError(
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"Tokenizer received was not an instance of PreTrainedTokenizerBase"
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)
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def _huggingface_tokenizer_length(text: str) -> int:
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return len(tokenizer.encode(text))
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except ImportError:
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raise ValueError(
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"Could not import transformers python package. "
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"Please install it with `pip install transformers`."
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)
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return cls(length_function=_huggingface_tokenizer_length, **kwargs)
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@classmethod
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def from_tiktoken_encoder(
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cls: type[TS],
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encoding_name: str = "gpt2",
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model_name: Optional[str] = None,
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allowed_special: Union[Literal["all"], Set[str]] = set(),
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disallowed_special: Union[Literal["all"], Collection[str]] = "all",
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**kwargs: Any,
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) -> TS:
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"""Text splitter that uses tiktoken encoder to count length."""
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate max_tokens_for_prompt. "
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"Please install it with `pip install tiktoken`."
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)
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if model_name is not None:
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enc = tiktoken.encoding_for_model(model_name)
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else:
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enc = tiktoken.get_encoding(encoding_name)
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def _tiktoken_encoder(text: str) -> int:
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return len(
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enc.encode(
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text,
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allowed_special=allowed_special,
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disallowed_special=disallowed_special,
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)
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)
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if issubclass(cls, TokenTextSplitter):
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extra_kwargs = {
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"encoding_name": encoding_name,
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"model_name": model_name,
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"allowed_special": allowed_special,
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"disallowed_special": disallowed_special,
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}
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kwargs = {**kwargs, **extra_kwargs}
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return cls(length_function=_tiktoken_encoder, **kwargs)
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def transform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Transform sequence of documents by splitting them."""
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return self.split_documents(list(documents))
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async def atransform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Asynchronously transform a sequence of documents by splitting them."""
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raise NotImplementedError
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class CharacterTextSplitter(TextSplitter):
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"""Splitting text that looks at characters."""
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def __init__(self, separator: str = "\n\n", **kwargs: Any) -> None:
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"""Create a new TextSplitter."""
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super().__init__(**kwargs)
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self._separator = separator
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def split_text(self, text: str) -> list[str]:
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"""Split incoming text and return chunks."""
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# First we naively split the large input into a bunch of smaller ones.
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splits = _split_text_with_regex(text, self._separator, self._keep_separator)
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_separator = "" if self._keep_separator else self._separator
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return self._merge_splits(splits, _separator)
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class LineType(TypedDict):
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"""Line type as typed dict."""
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metadata: dict[str, str]
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content: str
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class HeaderType(TypedDict):
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"""Header type as typed dict."""
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level: int
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name: str
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data: str
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class MarkdownHeaderTextSplitter:
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"""Splitting markdown files based on specified headers."""
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def __init__(
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self, headers_to_split_on: list[tuple[str, str]], return_each_line: bool = False
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):
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"""Create a new MarkdownHeaderTextSplitter.
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Args:
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headers_to_split_on: Headers we want to track
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return_each_line: Return each line w/ associated headers
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"""
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# Output line-by-line or aggregated into chunks w/ common headers
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self.return_each_line = return_each_line
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# Given the headers we want to split on,
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# (e.g., "#, ##, etc") order by length
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self.headers_to_split_on = sorted(
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headers_to_split_on, key=lambda split: len(split[0]), reverse=True
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)
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def aggregate_lines_to_chunks(self, lines: list[LineType]) -> list[Document]:
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"""Combine lines with common metadata into chunks
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Args:
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lines: Line of text / associated header metadata
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"""
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aggregated_chunks: list[LineType] = []
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for line in lines:
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if (
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aggregated_chunks
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and aggregated_chunks[-1]["metadata"] == line["metadata"]
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):
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# If the last line in the aggregated list
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# has the same metadata as the current line,
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# append the current content to the last lines's content
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aggregated_chunks[-1]["content"] += " \n" + line["content"]
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else:
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# Otherwise, append the current line to the aggregated list
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aggregated_chunks.append(line)
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return [
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Document(page_content=chunk["content"], metadata=chunk["metadata"])
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for chunk in aggregated_chunks
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]
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def split_text(self, text: str) -> list[Document]:
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"""Split markdown file
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Args:
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text: Markdown file"""
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# Split the input text by newline character ("\n").
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lines = text.split("\n")
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# Final output
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lines_with_metadata: list[LineType] = []
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# Content and metadata of the chunk currently being processed
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current_content: list[str] = []
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current_metadata: dict[str, str] = {}
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# Keep track of the nested header structure
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# header_stack: List[Dict[str, Union[int, str]]] = []
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header_stack: list[HeaderType] = []
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initial_metadata: dict[str, str] = {}
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for line in lines:
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stripped_line = line.strip()
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# Check each line against each of the header types (e.g., #, ##)
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for sep, name in self.headers_to_split_on:
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# Check if line starts with a header that we intend to split on
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if stripped_line.startswith(sep) and (
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# Header with no text OR header is followed by space
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# Both are valid conditions that sep is being used a header
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len(stripped_line) == len(sep)
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or stripped_line[len(sep)] == " "
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):
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# Ensure we are tracking the header as metadata
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if name is not None:
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# Get the current header level
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current_header_level = sep.count("#")
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# Pop out headers of lower or same level from the stack
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while (
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header_stack
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and header_stack[-1]["level"] >= current_header_level
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):
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# We have encountered a new header
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# at the same or higher level
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popped_header = header_stack.pop()
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# Clear the metadata for the
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# popped header in initial_metadata
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if popped_header["name"] in initial_metadata:
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initial_metadata.pop(popped_header["name"])
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# Push the current header to the stack
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header: HeaderType = {
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"level": current_header_level,
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"name": name,
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"data": stripped_line[len(sep):].strip(),
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}
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header_stack.append(header)
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# Update initial_metadata with the current header
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initial_metadata[name] = header["data"]
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# Add the previous line to the lines_with_metadata
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# only if current_content is not empty
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if current_content:
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lines_with_metadata.append(
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{
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"content": "\n".join(current_content),
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"metadata": current_metadata.copy(),
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}
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)
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current_content.clear()
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break
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else:
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if stripped_line:
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current_content.append(stripped_line)
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elif current_content:
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lines_with_metadata.append(
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{
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"content": "\n".join(current_content),
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"metadata": current_metadata.copy(),
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}
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)
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current_content.clear()
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current_metadata = initial_metadata.copy()
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if current_content:
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lines_with_metadata.append(
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{"content": "\n".join(current_content), "metadata": current_metadata}
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)
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# lines_with_metadata has each line with associated header metadata
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# aggregate these into chunks based on common metadata
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if not self.return_each_line:
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return self.aggregate_lines_to_chunks(lines_with_metadata)
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else:
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return [
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Document(page_content=chunk["content"], metadata=chunk["metadata"])
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for chunk in lines_with_metadata
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]
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# should be in newer Python versions (3.10+)
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# @dataclass(frozen=True, kw_only=True, slots=True)
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@dataclass(frozen=True)
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class Tokenizer:
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chunk_overlap: int
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tokens_per_chunk: int
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decode: Callable[[list[int]], str]
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encode: Callable[[str], list[int]]
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def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> list[str]:
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"""Split incoming text and return chunks using tokenizer."""
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splits: list[str] = []
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input_ids = tokenizer.encode(text)
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start_idx = 0
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cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
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chunk_ids = input_ids[start_idx:cur_idx]
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while start_idx < len(input_ids):
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splits.append(tokenizer.decode(chunk_ids))
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start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
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cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
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chunk_ids = input_ids[start_idx:cur_idx]
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return splits
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class TokenTextSplitter(TextSplitter):
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"""Splitting text to tokens using model tokenizer."""
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def __init__(
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self,
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encoding_name: str = "gpt2",
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model_name: Optional[str] = None,
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allowed_special: Union[Literal["all"], Set[str]] = set(),
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disallowed_special: Union[Literal["all"], Collection[str]] = "all",
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**kwargs: Any,
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) -> None:
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"""Create a new TextSplitter."""
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super().__init__(**kwargs)
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Could not import tiktoken python package. "
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"This is needed in order to for TokenTextSplitter. "
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"Please install it with `pip install tiktoken`."
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)
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if model_name is not None:
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enc = tiktoken.encoding_for_model(model_name)
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else:
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enc = tiktoken.get_encoding(encoding_name)
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self._tokenizer = enc
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self._allowed_special = allowed_special
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self._disallowed_special = disallowed_special
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def split_text(self, text: str) -> list[str]:
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def _encode(_text: str) -> list[int]:
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return self._tokenizer.encode(
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_text,
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allowed_special=self._allowed_special,
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disallowed_special=self._disallowed_special,
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)
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tokenizer = Tokenizer(
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chunk_overlap=self._chunk_overlap,
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tokens_per_chunk=self._chunk_size,
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decode=self._tokenizer.decode,
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encode=_encode,
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)
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return split_text_on_tokens(text=text, tokenizer=tokenizer)
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class RecursiveCharacterTextSplitter(TextSplitter):
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"""Splitting text by recursively look at characters.
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Recursively tries to split by different characters to find one
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that works.
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"""
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def __init__(
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self,
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separators: Optional[list[str]] = None,
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keep_separator: bool = True,
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**kwargs: Any,
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) -> None:
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"""Create a new TextSplitter."""
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super().__init__(keep_separator=keep_separator, **kwargs)
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self._separators = separators or ["\n\n", "\n", " ", ""]
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def _split_text(self, text: str, separators: list[str]) -> list[str]:
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"""Split incoming text and return chunks."""
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final_chunks = []
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# Get appropriate separator to use
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separator = separators[-1]
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new_separators = []
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for i, _s in enumerate(separators):
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if _s == "":
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separator = _s
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break
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if re.search(_s, text):
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separator = _s
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new_separators = separators[i + 1:]
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break
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splits = _split_text_with_regex(text, separator, self._keep_separator)
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# Now go merging things, recursively splitting longer texts.
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_good_splits = []
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_separator = "" if self._keep_separator else separator
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for s in splits:
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if self._length_function(s) < self._chunk_size:
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_good_splits.append(s)
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else:
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if _good_splits:
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merged_text = self._merge_splits(_good_splits, _separator)
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final_chunks.extend(merged_text)
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_good_splits = []
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if not new_separators:
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final_chunks.append(s)
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else:
|
|
other_info = self._split_text(s, new_separators)
|
|
final_chunks.extend(other_info)
|
|
if _good_splits:
|
|
merged_text = self._merge_splits(_good_splits, _separator)
|
|
final_chunks.extend(merged_text)
|
|
return final_chunks
|
|
|
|
def split_text(self, text: str) -> list[str]:
|
|
return self._split_text(text, self._separators)
|